Processing control in a streaming application

ABSTRACT

A method, system, and computer program product for processing a stream of tuples are disclosed. The method, system, and computer program product may include receiving a stream of tuples to be processed by a plurality of processing elements. Each tuple may have an associated processing history. The stream of tuples may be segmented into a plurality of partitions, each representing a subset of the stream of tuples. The method, system, and computer program product may include estimating the contribution each partition will have on a particular processing result and processing a partition if it substantially contributes to the particular processing result.

FIELD

This disclosure generally relates to stream computing, and inparticular, to computing applications that receive streaming data andprocess the data as it is received.

BACKGROUND

Database systems are typically configured to separate the process ofstoring data from accessing, manipulating, or using data stored in adatabase. More specifically, database systems use a model in which datais first stored and indexed in a memory before subsequent querying andanalysis. In general, database systems may not be well suited forperforming real-time processing and analyzing streaming data. Inparticular, database systems may be unable to store, index, and analyzelarge amounts of streaming data efficiently or in real time.

SUMMARY

Embodiments of the disclosure provide a method, system, and computerprogram product for processing data. The method, system, and computerprogram receive streaming data to be processed by a plurality ofprocessing elements comprising one or more stream operators.

One embodiment is directed to a method for processing a stream of tuplesin a streaming application. The method may include a mechanism forcontrolling whether to omit some of the processing in a streamingapplication. The method may include receiving a stream of tuples to beprocessed by a plurality of processing elements. Each tuple may have anassociated processing history. The stream of tuples may be segmentedinto a plurality of partitions, each representing a subset of the streamof tuples. The method may include estimating the contribution eachpartition will have on a particular processing result. The method mayalso include processing a partition if it substantially contributes tothe particular processing result.

Another embodiment is directed to a system for processing a stream oftuples in a streaming application. The system may include a mechanismfor controlling whether to omit some of the processing in a streamingapplication. The system may include receiving a stream of tuples to beprocessed by a plurality of processing elements. Each tuple may have anassociated processing history. The stream of tuples may be segmentedinto a plurality of partitions, each representing a subset of the streamof tuples. The system may include estimating the contribution eachpartition will have on a particular processing result. The system mayalso provide for processing a partition if it substantially contributesto the particular processing result.

Yet another embodiment is directed to a computer program product.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application, according to various embodiments.

FIG. 2 illustrates a more detailed view of a compute node of FIG. 1,according to various embodiments.

FIG. 3 illustrates a more detailed view of the management system of FIG.1, according to various embodiments.

FIG. 4 illustrates a more detailed view of the compiler system of FIG.1, according to various embodiments.

FIG. 5 illustrates an operator graph for a stream computing application,according to various embodiments.

FIG. 6 illustrates a method to control processing in a streamingapplication, according to various embodiments.

FIG. 7 illustrates a method to determine whether to omit processing fora partition in a streaming application, according to variousembodiments.

FIG. 8 illustrates a section of an operator graph for a streamingapplication that is configured with the method to determine whether toomit processing for a partition, according to various embodiments.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream-based computing application, stream operators are connectedto one another such that data flows from one stream operator to the next(e.g., over a TCP/IP socket). Scalability is achieved by distributing anapplication across nodes by creating executables (i.e., processingelements), as well as replicating processing elements on multiple nodesand load balancing among them. Stream operators in a stream computingapplication can be fused together to form a processing element that isexecutable. Doing so allows processing elements to share a commonprocess space, resulting in much faster communication between streamoperators than is available using inter-process communication techniques(e.g., using a TCP/IP socket). Further, processing elements can beinserted or removed dynamically from an operator graph representing theflow of data through the stream computing application.

A “tuple” is data. More specifically, a tuple is a sequence of one ormore attributes associated with an entity. Attributes may be any of avariety of different types, e.g., integer, float, Boolean, string, etc.The attributes may be ordered. A tuple may be extended by adding one ormore additional attributes to it. In addition to attributes associatedwith an entity, a tuple may include metadata, i.e., data about thetuple. As used herein, “stream” or “data stream” refers to a sequence oftuples. Generally, a stream may be considered a pseudo-infinite sequenceof tuples.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.

In some embodiments, a streaming application may be configured tocomplete less than all of its processing while still providing a resultof that processing that is representative of the entire processing. Thatis, the streaming application may be able to determine some portion ofthe processing that may be omitted and approximate the result that wouldotherwise be achieved if the entire processing were completed. In someembodiments, the portion of the processing that is omitted is determinedin a way that maintains a high confidence level that the remainingprocessing provides an end result that is statistically representativeof the end result when all processing is completed. In otherembodiments, the streaming application may estimate the contribution ofsome portion of the processing and select portions of the processingthat may substantially affect the end result.

In some embodiments, the method to control processing may be enabledwhen one or more performance metrics of the streaming application falloutside a threshold. For example, if the performance metric trackslatency of a tuple and the latency metric is above a certain threshold,the streaming application may not be processing data as quickly asdesired. Accordingly, disabling some portion of the processing mayimprove the latency, which may improve the performance of the entirestreaming application. In some embodiments, the streaming applicationmay select the portion to omit from processing, while in otherembodiments the streaming application may select the portions to includein processing, which may in turn omit some portions of the processing.In some embodiments, processing may be omitted to optimize theperformance of the streaming application, such as maintaining a lowlatency metric during runtime.

“Latency,” as used herein, may refer to the amount of time a particulartuple is at a processing element. That is, the latency may be the timedifference between the timestamp at which a tuple is received at aprocessing element and the timestamp at which the tuple is output fromthe processing element, including time in which the tuple is waiting tobe processed.

A streaming application may be configured to concurrently process aplurality of tuples. Concurrent processing may be accomplished bysegmenting a data stream into a plurality of partitions, therebydistributing the load across multiple hosts. A “partition” may beconsidered to be a subset of a data stream. The partitioning may bebased on one or more values of the incoming tuples in some embodiments,as specified by an application programmer. For example, a stream oftuples containing information about employees may be partitioned basedon an attribute containing department numbers. In some embodiments, oncethe plurality of partitions are processed, the resulting tuples may bejoined together to aggregate the individual results into the end resultthat was the goal of the processing of the streaming application. Inother embodiments, each partition may provide an individual result.

In some embodiments, segmenting based on the one or more values maygenerate a large number of partitions and therefore a significant amountof concurrent processing. In such a case, concurrently processing thetuples may negatively impact the performance of the streamingapplication. There may, however, be one or more partitions that do notsubstantially contribute to the end result that is the goal of theprocessing. For example, a first partition may not substantiallycontribute to a desired result if it is substantially similar to asecond partition. The end result of processing a first partition may besubstantially similar to the end result of processing a second partitionif the end results of processing the partitions are statisticallysimilar. If the end result of processing with the modified processing iswithin a threshold confidence level, the omitted partition may notsubstantially contribute to the end result of the full processing.

A “confidence level” may be used to determine whether a partitionsubstantially contributes to an end result. A “confidence level,” insome embodiments, may require a statistical comparison of the end resultwith all partitions processed and the end result with less than allpartitions processed to be within some range of each other. In someembodiments, a confidence factor may also be used to refer to aconfidence level. The confidence level may be a system default valuethat is capable of being overridden by an application programmer.Reducing the amount of processing a streaming application executes inorder to obtain results may, in some embodiments, improve its overallperformance. The processing results of one or more of the remainingpartitions may be considered to be representative, or a statisticalrepresentation within a threshold confidence level, of the processingresults for the entire data stream even though some portion of the datais not being processed.

Eliminating processing of partitions may, in some embodiments, beaccomplished through the use of a processing history that may bemaintained by the streaming application. For example, a streamingapplication may maintain a processing history that includes input tuplevalues for a partition, output tuple values for a partition, and the endresult obtained by processing the various partitions. In someembodiments, the processing history of the tuples may be used tomaintain a partition processing history. The processing history may, forexample, be used to determine to omit processing for a partition thatcontains substantially similar output tuple values for a plurality ofpartitions. In other embodiments, the processing history may be used topredict which partitions may yield a processing result that isrepresentative, within a threshold confidence level, of the entireprocessing. For example, a first stream operator may ask for informationbased on criteria A and need partitions 1, 2, and 3 to provide arepresentative result, while a second stream operator may ask forinformation based on criteria B and need partitions 3 and 4. A thirdstream operator that queries information based on criteria A and B mayyield a representative answer with partitions 1, 2, 3, and 4 (acombination of A and B), which may be predicted based on the processinghistories of the first and second stream operators.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream-based computing application, accordingto some embodiments. The computing infrastructure 100 includes amanagement system 105 and two or more compute nodes 110A-110D—i.e.,hosts—which are communicatively coupled to each other using one or morecommunications networks 120. The communications network 120 may includeone or more servers, networks, or databases, and may use a particularcommunication protocol to transfer data between the compute nodes110A-110D. A compiler system 102 may be communicatively coupled with themanagement system 105 and the compute nodes 110 either directly or viathe communications network 120.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A streams application may include one or more stream operators 240 thatmay be compiled into a “processing element” container 235. The memory225 may include two or more processing elements 235, each processingelement having one or more stream operators 240. Each stream operator240 may include a portion of code that processes tuples flowing into aprocessing element and outputs tuples to other stream operators 240 inthe same processing element, in other processing elements, or in boththe same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. Although shown as being instorage, the buffer 260 may be located in the memory 225 of the computenode 110 or in a combination of both memories. Moreover, storage 230 mayinclude storage space that is external to the compute node 110, such asin a cloud.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) forprocessing.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe streaming application. In some embodiments, the compiler 136 may bea just-in-time compiler that executes as part of an interpreter. Inother embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. Although FIG. 5 is abstracted to show connectedprocessing elements PE1-PE10, the operator graph 500 may include dataflows between stream operators 240 (FIG. 2) within the same or differentprocessing elements. Typically, processing elements, such as processingelement 235 (FIG. 2), receive tuples from the stream as well as outputtuples into the stream (except for a sink—where the stream terminates,or a source—where the stream begins).

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 504. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 504. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 506.

The tuple received by a particular processing element 235 (FIG. 2) isgenerally not considered to be the same tuple that is output downstream.Typically, the output tuple is changed in some way. An attribute ormetadata may be added, deleted, or changed. However, it is not requiredthat the output tuple be changed in some way. Generally, a particulartuple output by a processing element may not be considered to be thesame tuple as a corresponding input tuple even if the input tuple is notchanged by the processing element. However, to simplify the presentdescription and the claims, an output tuple that has the same dataattributes as a corresponding input tuple may be referred to herein asthe same tuple.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

FIG. 6 illustrates a method 600 to control processing in a streamingapplication, according to various embodiments. In some embodiments,“controlling processing” may include determining whether to omit some ofthe processing of a streaming application. In other embodiments,“controlling processing” may include determining which portions of astreaming application should be processed. “Omitting processing” mayinclude disabling processing for one or more partitions. In otherembodiments, “omitting processing” may include deactivating processing,truncating processing, or other similar methods of omitting a portion ofthe processing of the streaming application. As described above, apartition may be a segment (or subset) of an incoming data stream, andmay, for example, be based on one or more attribute values of a tuple.In some embodiments, omitting processing for one or more partitions mayminimize CPU and system resource constraints. In other embodiments,omitting processing for one or more partitions may also improve theperformance, which may, for example, be measured by latency for a tuple.

The method 600 may be configured to execute upon various conditions. Forexample, the method 600 may be enabled once per day, may be enabled inaccordance with a windowing condition, or may be enabled or disabledusing a configuration file that specifies various rules. A rule forenabling or disabling the method 600 may be configurable, and mayinclude, for example, conditions based on tuple counts, addition orremoval of hardware, time of day, latency metrics, or other similarconditions. In other embodiments, such a rule may be based on resourceconstraints. In yet other embodiments, the method 600 may be enabled bydefault to attempt to maximize the performance of a streamingapplication. As previously mentioned, the method 600 may use a windowingcondition as bounds for enabling and disabling the method. For example,the method may omit processing during the life of the window, but uponexpiration of the window, the omitted processing will be reactivated.

A “window,” as referred to herein, is a logical container for tuplesreceived by an input port of a stream operator. Windowing may allow forcreation of subsets of data within a streaming application. A streamoperator may not necessarily support windowing by default. A streamoperator may, however, be configured to support windowing. Both tumblingand sliding windows may store tuples according to various conditions. Atumbling window may store incoming tuples until the window is full, thenmay trigger a stream operator behavior, flush all stored tuples from thewindow, and then may begin this process again. Conversely, a slidingwindow does not automatically flush the window when the triggercondition is fulfilled. A sliding window also has an eviction policythat tells the window when to flush the window and begin this processagain. These conditions may be referred to herein as windowingconditions. Windowing may be defined in any number of ways. For example,an application programmer may define one or more specific windowingconditions. Additionally, the system may provide a set of windowingconditions.

Generally, the method 600 may include monitoring the processing historyof a streaming application and determining whether to omit processingbased on the processing history. The processing history may, in someembodiments, be stored in a data store, e.g., a database or memorystructure, that is accessible to the stream manager 134 In someembodiments, this may include processing one or more tuples to determinewhich partitions substantially contribute to an end result of astreaming application. In other embodiments, the streaming applicationmay, at some interval, determine whether the modified processing isstill representative of the whole processing.

The method 600 may, in some embodiments, process a stream of tuples todetermine which partitions are representative of the end result. Themethod 600 may then determine to omit processing of partitions that donot substantially contribute to the end result. In other embodiments,the method 600 may omit processing for partitions that are notdeterminative of an end result. In yet other embodiments, the method 600may omit processing for partitions that are not statisticallysignificant in determining an end result.

The method 600 may begin at operation 605 with one or more streamoperators processing incoming tuples. Operation 605 may includecompleting all processing according to the configuration of thestreaming application. Operation 610 may record the processing historyfor a tuple as it is processed in operation 605. For example, the inputvalues and output values for a given tuple may be maintained in theprocessing history. In some embodiments, the processing history mayinclude additional information about how the tuple was processed. Inother embodiments, the processing history for a given tuple may be usedto populate the processing history for a particular partition.

At operation 615, the method 600 may determine partitions that shouldnot be processed and omit that processing, according to someembodiments. Partitions that should not be processed may be those thatdo not substantially contribute to the end processing result. In otherembodiments, partitions that should not be processed are those that arenot determinative of the end result. In yet other embodiments,partitions that should not be processed are those that are notstatistically significant in determining the result. Examples ofpartitions that either may not substantially contribute to the result orthat are not outcome determinative may include partitions that havevalues that are similar to another partition (within a standarddeviation that may be system provided, but capable of being overriddenby an application programmer) or partitions that have values that aresubstantially smaller than the values of another partition, such thatthere is little effect on the end processing result. The details of thedecision process will be described in greater detail in accordance withFIG. 7 below.

An example of a partition that may be omitted from processing may beobserved in a streaming application that completes processing onincoming stock information. The streaming application may be configuredto segment the data stream into partitions according to the sector inwhich the corporation belongs, such as industrial, healthcare,technology, utilities, etc. The end result of the processing may be somesort of indication of how a particular industry is performing. In such astreaming application, the processing history may indicate that theindustrial and utilities sectors track each other, within a thresholddeviation. Accordingly, the processing for one of the sectors may beomitted because the end result of the other sector's processing may berepresentative of both sectors. In some embodiments, the thresholddeviation may be applied to the end result.

At operation 620, the method 600 may determine whether the omittedprocessing should be completed, according to some embodiments. Thedetermination of operation 620 is a method of ensuring that the modifiedprocessing (with omitted processing) continues to provide processingresults that are representative of the entire data. The determination ofoperation 620 may be triggered in various manners. For example, in someembodiments the determination can be made at a time interval. In otherembodiments, the determination can be based upon a confidence level. Insuch an embodiment, the system may provide a default confidence levelthat is capable of being overridden by an application programmer. If theend result falls outside the confidence level, then the determination ofoperation 620 may indicate that all processing should be completed. Ifthe result of the determination at operation 620 is that the modifiedprocessing is still representative of the full processing, then themodified processing may continue 630. If the result of the determinationis that the omitted processing should be completed, then all processing(including the omitted partitions) will be completed again at operation625.

FIG. 7 illustrates a method 700 to determine whether to omit processinga partition in a streaming application, according to variousembodiments. As mentioned above, the method 700 may further describeoperation 615 of the method 600. Generally, the method 700 describes themanner in which a streaming application may determine whether or not toomit processing for a partition.

The method 700 may begin at operation 705, when a stream operatorreceives an incoming tuple. Upon receiving a tuple, the stream operatormay determine whether there is a processing history at operation 710.The determination of operation 710 may be based on the attribute valueor values that are used to segment the incoming data stream intomultiple partitions. While the determination of operation 710 iscurrently discussed as being performed by a stream operator, thefunctionality may also be performed by the stream manager 134 in someembodiments. In other embodiments, the stream manager 134 may performsome of the functionality while the particular stream operator performsthe rest of the functionality of operation 710. For simplicity, theoperations will be considered to be performed by a stream operatorherein.

If there is no processing history identified in operation 710, the tuplemay be processed by operation 715 and the processing history may bemodified accordingly at operation 720. If, however, a processing historyis identified in operation 710, the method 700 may continue withoperation 725, in which the stream operator determines whether the tupleshould be processed. Operation 725 may include multiple determinationsin order to determine whether or not to omit processing.

At operation 725, the stream operator may determine whether processingfor the partition corresponding to the particular tuple has already beenomitted. In other embodiments, operation 725 may determine whether thepartition includes one or more output values that are duplicative ofother partitions. In yet other embodiments, operation 725 may include adetermination of whether the partition corresponding to the particulartuple may substantially contribute to the end result. A tuple maysubstantially contribute to the end result if it is statisticallysignificant to determining the end result in some embodiments. In otherembodiments, the particular tuple may substantially contribute to theend result if it is outcome determinative of the end result. In yetother embodiments, the particular tuple may substantially contribute tothe end result if omitting processing of that partition would cause theend result to fall outside the threshold confidence level.

If, at operation 725, the determination is made that the particulartuple should be processed, the tuple will be processed according to theconfiguration of the streaming application in operation 730, and theprocessing history will be updated in operation 735. If, however,operation 725 results in a determination that the particular tupleshould not be processed, the processing will be omitted in operation740.

FIG. 8 illustrates a section of an operator graph 800 for a streamingapplication, according to some embodiments. The operations of methods600 and 700 may be further described through an example streamingapplication, which will be discussed in accordance with FIG. 8. Forexample, a streaming application may receive data from sensors inautomobiles as inputs via source 135. The data may, for example, includeinformation about the speed and location of an automobile. The dataoutput by each automobile may include additional information as well.Some type of processing may be completed at stream operator 802, andstream operator 804 may segment the data stream into partitions, asdescribed above. FIG. 8 shows two partitions 852, 854, plus arepresentative partition 856 to show that there may be N differentpartitions of a data stream, where N is based on a specific streamingapplication. Using the automobile data example, the streamingapplication may be configured to provide an end result at sink 840 thatrepresents the average speed of automobiles across the major UShighways. Such a streaming application may, for example, be configuredto include one partition per zip code. Each partition may includeadditional stream operators 806, 808, 810, 812, 814, and 816 thatcomplete some type of processing according to the configuration of theparticular streaming application. It may be possible to only processdata from a subset of the zip codes and still have an end processingresult that is representative of the answer where all processing iscompleted. For example, there may be zip codes that do not include anymajor highways, or that generally have so few cars that the end resultis not substantially affected by omitting the processing.

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Furthermore, although embodiments of this disclosure mayachieve advantages over other possible solutions or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of this disclosure. Thus, the described aspects,features, embodiments, and advantages are merely illustrative and arenot considered elements or limitations of the appended claims exceptwhere explicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination thereof. In the context ofthis disclosure, a computer readable storage medium may be any tangiblemedium that can contain, or store, a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire line, optical fiber cable, RF, etc., or any suitable combinationthereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including: (a) an object oriented programminglanguage; (b) conventional procedural programming languages; and (c) astreams programming language, such as IBM Streams Processing Language(SPL). The program code may execute as specifically described herein. Inaddition, the program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer, or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the present disclosure have been described with reference toflowchart illustrations, block diagrams, or both, of methods,apparatuses (systems), and computer program products according toembodiments of this disclosure. It will be understood that each block ofthe flowchart illustrations or block diagrams, and combinations ofblocks in the flowchart illustrations or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsor acts specified in the flowchart or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function or act specified in the flowchart or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions or acts specified in the flowchart or blockdiagram block or blocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams or flowchart illustration, andcombinations of blocks in the block diagrams or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

Although embodiments are described within the context of a streamcomputing application, this is not the only context relevant to thepresent disclosure. Instead, such a description is without limitationand is for illustrative purposes only. Additional embodiments may beconfigured to operate with any computer system or application capable ofperforming the functions described herein. For example, embodiments maybe configured to operate in a clustered environment with a standarddatabase processing application. A multi-nodal environment may operatein a manner that effectively processes a stream of tuples. For example,some embodiments may include a large database system, and a query of thedatabase system may return results in a manner similar to a stream ofdata.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the disclosure may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow.

1. A method for processing a stream of tuples, comprising: receiving astream of tuples, each tuple having an associated processing history, tobe processed by a plurality of processing elements; segmenting thestream of tuples into a plurality of partitions, wherein a partitionrepresents a subset of the stream of tuples; estimating a contributioneach partition will have on a particular processing result based on thepartition and the associated processing history; and processing apartition if it substantially contributes to the particular processingresult.
 2. The method of claim 1, wherein the estimating is enabled whena performance metric falls outside a performance threshold.
 3. Themethod of claim 1, wherein the estimating is enabled within a window. 4.The method of claim 1, further comprising resuming processing of anomitted partition when a confidence level falls outside a modificationthreshold.
 5. The method of claim 1, further comprising resumingprocessing of an omitted partition at a time interval.
 6. The method ofclaim 1, wherein a partition substantially contributes to the processingresult if a confidence level falls outside a modification threshold. 7.The method of claim 1, wherein a partition substantially contributes toa processing result when the processing history indicates a tupleattribute value for a first partition is outside a deviation thresholdfrom a tuple attribute value for a second partition.
 8. The method ofclaim 1, further comprising generating a combined partition processinghistory from the associated processing history of the tuples in thepartition. 9-20. (canceled)